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Distributed state estimation of moving targets using cyclic simultaneous perturbation stochastic approximation. / Erofeeva, Victoria; Granichin, Oleg.

In: IFAC-PapersOnLine, Vol. 51, No. 23, 30.08.2018, p. 218-223.

Research output: Contribution to journal › Article › peer-review

Harvard

Erofeeva, V & Granichin, O 2018, 'Distributed state estimation of moving targets using cyclic simultaneous perturbation stochastic approximation', IFAC-PapersOnLine, vol. 51, no. 23, pp. 218-223. https://doi.org/10.1016/j.ifacol.2018.12.038

APA

Erofeeva, V., & Granichin, O. (2018). Distributed state estimation of moving targets using cyclic simultaneous perturbation stochastic approximation. IFAC-PapersOnLine, 51(23), 218-223. https://doi.org/10.1016/j.ifacol.2018.12.038

Vancouver

Erofeeva V, Granichin O. Distributed state estimation of moving targets using cyclic simultaneous perturbation stochastic approximation. IFAC-PapersOnLine. 2018 Aug 30;51(23):218-223. https://doi.org/10.1016/j.ifacol.2018.12.038

Author

Erofeeva, Victoria ; Granichin, Oleg. / Distributed state estimation of moving targets using cyclic simultaneous perturbation stochastic approximation. In: IFAC-PapersOnLine. 2018 ; Vol. 51, No. 23. pp. 218-223.

BibTeX

@article{1492c481f6854948a5868d246e1d8945,
title = "Distributed state estimation of moving targets using cyclic simultaneous perturbation stochastic approximation",
abstract = "Sensor networks comprised of multiple nodes with sensing, processing and communication capabilities are ubiquitous in tracking systems. However, when a tracking system is required to track a large number of targets, the computation and communication loads arise. One possible solution is to use a distributed scheme. In this paper we propose a distributed multiple target tracking algorithm, which takes into account restrictions on the sensor network functioning. Our method is based on the modification of Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm with a perturbation on the input. For the proposed algorithm we get an upper bound of residual between estimates and show the simulation results.",
keywords = "communication constraints, computational constraints, distributed estimation, sensor network, stochastic approximation, target tracking",
author = "Victoria Erofeeva and Oleg Granichin",
year = "2018",
month = aug,
day = "30",
doi = "10.1016/j.ifacol.2018.12.038",
language = "English",
volume = "51",
pages = "218--223",
journal = "IFAC-PapersOnLine",
issn = "2405-8971",
publisher = "Elsevier",
number = "23",

}

RIS

TY - JOUR

T1 - Distributed state estimation of moving targets using cyclic simultaneous perturbation stochastic approximation

AU - Erofeeva, Victoria

AU - Granichin, Oleg

PY - 2018/8/30

Y1 - 2018/8/30

N2 - Sensor networks comprised of multiple nodes with sensing, processing and communication capabilities are ubiquitous in tracking systems. However, when a tracking system is required to track a large number of targets, the computation and communication loads arise. One possible solution is to use a distributed scheme. In this paper we propose a distributed multiple target tracking algorithm, which takes into account restrictions on the sensor network functioning. Our method is based on the modification of Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm with a perturbation on the input. For the proposed algorithm we get an upper bound of residual between estimates and show the simulation results.

AB - Sensor networks comprised of multiple nodes with sensing, processing and communication capabilities are ubiquitous in tracking systems. However, when a tracking system is required to track a large number of targets, the computation and communication loads arise. One possible solution is to use a distributed scheme. In this paper we propose a distributed multiple target tracking algorithm, which takes into account restrictions on the sensor network functioning. Our method is based on the modification of Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm with a perturbation on the input. For the proposed algorithm we get an upper bound of residual between estimates and show the simulation results.

KW - communication constraints

KW - computational constraints

KW - distributed estimation

KW - sensor network

KW - stochastic approximation

KW - target tracking

UR - http://www.scopus.com/inward/record.url?scp=85058489483&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2018.12.038

DO - 10.1016/j.ifacol.2018.12.038

M3 - Article

AN - SCOPUS:85058489483

VL - 51

SP - 218

EP - 223

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

IS - 23

ER -

ID: 35254320